本文档详细介绍了如何在Ubuntu16.04系统上安装IntelGPUOpenCL驱动，并通过编译OpenCV3.4.14来验证驱动的正确安装。首先，从Github下载适用于Ubuntu16.04的OpenCL驱动，并安装deb包。然后，使用clinfo确认驱动安装成功。接着，配置编译环境，下载OpenCV源码并进行编译。最后，编译并运行一个简单的OpenCV程序来检测OpenCL支持。
文章目录
编译环境
- 系统环境：Ubuntu 16.04.7 xenial LTS Desktop
- CPU：Intel® Core™ i7-6700 CPU @ 3.40GHz
安装Intel GPU OpenCL驱动
在[Github - intel/compute-runtime]上下载支持Ubuntu16.04版本的驱动，我这里下载的版本是18.45.11804，可点击[此处]直接下载对应版本。
此版本支持的CPU架构详情如下：
| Platform | OCL | Quality |
|---|---|---|
| Broadwell | 2.1 | Production |
| Skylake | 2.1 | Production |
| Kaby Lake | 2.1 | Production |
| Coffee Lake | 2.1 | Production |
| Cannon Lake | 2.1 | Experimental |
| Apollo Lake | 1.2 | Production |
| Gemini Lake | 1.2 | Production |
创建neo目录
mkdir neo # 在磁盘上找个位置，创建一个空的neo文件夹
下载deb安装包
cd neo
wget https://github.com/intel/compute-runtime/releases/download/18.45.11804/intel-gmmlib_18.4.0.348_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/18.45.11804/intel-igc-core_18.44.1060_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/18.45.11804/intel-igc-opencl_18.44.1060_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/18.45.11804/intel-opencl_18.45.11804_amd64.deb
以root权限安装deb包
sudo dpkg -i *.deb
安装clinfo
sudo apt-get install clinfo
查看Intel GPU OpenCL驱动信息
Number of platforms 1
Platform Name Intel(R) OpenCL HD Graphics # 成功识别到Intel(R) OpenCL HD Graphics
Platform Vendor Intel(R) Corporation
Platform Version OpenCL 2.1
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_fp16 cl_khr_depth_images cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_icd cl_khr_image2d_from_buffer cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_intel_subgroups cl_intel_required_subgroup_size cl_intel_subgroups_short cl_khr_spir cl_intel_accelerator cl_intel_media_block_io cl_intel_driver_diagnostics cl_intel_device_side_avc_motion_estimation cl_khr_priority_hints cl_khr_throttle_hints cl_khr_create_command_queue cl_khr_fp64 cl_khr_subgroups cl_khr_il_program cl_khr_mipmap_image cl_khr_mipmap_image_writes cl_intel_planar_yuv cl_intel_packed_yuv cl_intel_motion_estimation cl_intel_advanced_motion_estimation
Platform Host timer resolution 1ns
Platform Extensions function suffix INTEL
Platform Name Intel(R) OpenCL HD Graphics
Number of devices 1
Device Name Intel(R) Gen9 HD Graphics NEO
Device Vendor Intel(R) Corporation
Device Vendor ID 0x8086
Device Version OpenCL 2.1 NEO
Driver Version 18.45.11804 # 驱动版本与下载的版本一致
Device OpenCL C Version OpenCL C 2.0
Device Type GPU
Device Profile FULL_PROFILE
Max compute units 24
Max clock frequency 1150MHz
Device Partition (core)
Max number of sub-devices 0
Supported partition types None
Max work item dimensions 3
Max work item sizes 256x256x256
Max work group size 256
Preferred work group size multiple 32
Max sub-groups per work group 32
Preferred / native vector sizes
char 16 / 16
short 8 / 8
int 4 / 4
long 1 / 1
half 8 / 8 (cl_khr_fp16)
float 1 / 1
double 1 / 1 (cl_khr_fp64)
Half-precision Floating-point support (cl_khr_fp16)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations No
Single-precision Floating-point support (core)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations Yes
Double-precision Floating-point support (cl_khr_fp64)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations No
Address bits 64, Little-Endian
Global memory size 13346914304 (12.43GiB)
Error Correction support No
Max memory allocation 4294959104 (4GiB)
Unified memory for Host and Device Yes
Shared Virtual Memory (SVM) capabilities (core)
Coarse-grained buffer sharing Yes
Fine-grained buffer sharing No
Fine-grained system sharing No
Atomics No
Minimum alignment for any data type 128 bytes
Alignment of base address 1024 bits (128 bytes)
Preferred alignment for atomics
SVM 64 bytes
Global 64 bytes
Local 64 bytes
Max size for global variable 65536 (64KiB)
Preferred total size of global vars 4294959104 (4GiB)
Global Memory cache type Read/Write
Global Memory cache size 524288
Global Memory cache line 64 bytes
Image support Yes
Max number of samplers per kernel 16
Max size for 1D images from buffer 268434944 pixels
Max 1D or 2D image array size 2048 images
Base address alignment for 2D image buffers 4 bytes
Pitch alignment for 2D image buffers 4 bytes
Max 2D image size 16384x16384 pixels
Max 3D image size 16384x16384x2048 pixels
Max number of read image args 128
Max number of write image args 128
Max number of read/write image args 128
Max number of pipe args 16
Max active pipe reservations 1
Max pipe packet size 1024
Local memory type Local
Local memory size 65536 (64KiB)
Max constant buffer size 4294959104 (4GiB)
Max number of constant args 8
Max size of kernel argument 1024
Queue properties (on host)
Out-of-order execution Yes
Profiling Yes
Queue properties (on device)
Out-of-order execution Yes
Profiling Yes
Preferred size 131072 (128KiB)
Max size 67108864 (64MiB)
Max queues on device 1
Max events on device 1024
Prefer user sync for interop Yes
Profiling timer resolution 83ns
Execution capabilities
Run OpenCL kernels Yes
Run native kernels No
Sub-group independent forward progress Yes
IL version SPIR-V_1.0
SPIR versions 1.2
printf() buffer size 4194304 (4MiB)
Built-in kernels block_motion_estimate_intel;block_advanced_motion_estimate_check_intel;block_advanced_motion_estimate_bidirectional_check_intel;
Motion Estimation accelerator version (Intel) 2
Device Available Yes
Compiler Available Yes
Linker Available Yes
Device Extensions cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_fp16 cl_khr_depth_images cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_icd cl_khr_image2d_from_buffer cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_intel_subgroups cl_intel_required_subgroup_size cl_intel_subgroups_short cl_khr_spir cl_intel_accelerator cl_intel_media_block_io cl_intel_driver_diagnostics cl_intel_device_side_avc_motion_estimation cl_khr_priority_hints cl_khr_throttle_hints cl_khr_create_command_queue cl_khr_fp64 cl_khr_subgroups cl_khr_il_program cl_khr_mipmap_image cl_khr_mipmap_image_writes cl_intel_planar_yuv cl_intel_packed_yuv cl_intel_motion_estimation cl_intel_advanced_motion_estimation
NULL platform behavior
clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...) Intel(R) OpenCL HD Graphics
clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...) Success [INTEL]
clCreateContext(NULL, ...) [default] Success [INTEL]
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU) Success (1)
Platform Name Intel(R) OpenCL HD Graphics
Device Name Intel(R) Gen9 HD Graphics NEO
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL) Success (1)
Platform Name Intel(R) OpenCL HD Graphics
Device Name Intel(R) Gen9 HD Graphics NEO
ICD loader properties
ICD loader Name OpenCL ICD Loader
ICD loader Vendor OCL Icd free software
ICD loader Version 2.2.8
ICD loader Profile OpenCL 1.2
NOTE: your OpenCL library declares to support OpenCL 1.2,
but it seems to support up to OpenCL 2.1 too.
编译OpenCV
配置编译环境
sudo apt install build-essential
sudo apt install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt install python-dev python-numpy libtbb2 libtbb-dev libjasper1 libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
下载OpenCV 3.4.14源码
wget https://github.com/opencv/opencv/archive/refs/tags/3.4.14.tar.gz -O OpenCV-3.4.14.tar.gz # 下载源码包并重命名
tar -xzvf OpenCV-3.4.14.tar.gz # 解压源码压缩包
配置编译选项
cd opencv-3.4.14 # 进入源码目录
mkdir build && cd build # 创建build目录并进入
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/data/opencv-3.4.14-opencl \ # 配置安装路径
-D BUILD_SHARED_LIBS=OFF \
-D BUILD_SHARED_LIBS=NO \
-D BUILD_PNG=ON \
-D BUILD_JASPER=ON \
-D BUILD_JPEG=ON \
-D BUILD_TIFF=ON \
-D BUILD_ZLIB=ON \
-D WITH_JPEG=ON \
-D WITH_PNG=ON \
-D WITH_JASPER=ON \
-D WITH_TIFF=ON \
-D WITH_TBB=ON \ # 启用TBB并行化
-D WITH_ZLIB=ON \
-D WITH_OPENCL=ON .. # 记得这里有两个点
开始编译OpenCV
make -j8 # 并发线程设置为8
安装
make install # 这里安装的目录是上面配置的安装路径/data/opencv-3.4.14-opencl，根据需要自己修改安装路径
测试Demo
testopencv.cpp源代码
#include <iostream>
#include "opencv2/opencv.hpp"
#include "opencv2/core/ocl.hpp"
int main()
{
cv::ocl::setUseOpenCL(true);
if (!cv::ocl::haveOpenCL())
{
std::cout << "OpenCL is not available..." << std::endl;
return 0;
}
cv::ocl::Context context;
if (!context.create(cv::ocl::Device::TYPE_GPU))
{
std::cout << "Failed creating the context..." << std::endl;
return 0;
}
std::cout << context.ndevices() << " GPU devices are detected." << std::endl; //This bit provides an overview of the OpenCL devices you have in your computer
for (int i = 0; i < context.ndevices(); i++)
{
cv::ocl::Device device = context.device(i);
std::cout << "name: " << device.name() << std::endl;
std::cout << "available: " << device.available() << std::endl;
std::cout << "imageSupport: " << device.imageSupport() << std::endl;
std::cout << "OpenCL_C_Version: " << device.OpenCL_C_Version() << std::endl;
std::cout << std::endl;
}
return 1;
}
编译Demo
export PKG_CONFIG_PATH=/data/opencv-3.4.14-opencl/lib/pkgconfig/:$PKG_CONFIG_PATH
g++ testopencv.cpp `pkg-config --cflags --libs opencv` -pthread
运行Demo
./a.out
结果如下：
以上运行结果说明，已成功通过OpenCV中的OpenCL模块检测到Intel的集成显卡(HD Graphics)！
特别说明：在OpenCV3中，已经嵌入了OpenCL运行的方式，通过使用UMat对象，OpenCV会自动在支持OpenCL的设备上使用GPU运算，在不支持OpenCL的设备仍然使用CPU运算。
可通过命令查看Intel GPU使用率：
sudo intel_gpu_top
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